def standalone_get_subgraphs(inputs, score_attribute, group, threshold, min_size, max_size):
flatter = Flatten()
tcc = ThresholdedConnectedComponents(attribute=score_attribute, more_than=False, shrink_graphs=True,
threshold=threshold,
min_size=min_size,
max_size=max_size)
for e in flatter.transform(tcc.transform(inputs)):
yield e
def _transform_single(self, graph):
'''
Parameters
----------
score_attribute: string
name of the attribute used
group: string
annnotate in this field
Returns
-------
'''
graphcopy = graph.copy()
maxnodeid = max(graph.nodes())
# def f(n,d): d[score_attribute] = graph.degree(n)
# node_operation(graph,f)
tcc = ThresholdedConnectedComponents(attribute=self.score_attribute, more_than=False, shrink_graphs=True)
components = tcc._extract_ccomponents(graph, threshold=self.score_threshold, min_size=self.min_size,
max_size=self.max_size)
nodeset = {n for g in components for n in g.nodes()}
def f(n, d):
d[self.group_attribute] = '~' if n in nodeset else '-'
node_operation(graph, f)
# now we either contract what we have, or additionally rename the contracted nodes according to the group estimator
graph = name_estimation(graph, self.group_attribute, self.layer, graphcopy, self.vectorizer, self.nameestimator,
components)
#else:
# graph = contraction([graph],
# contraction_attribute=group,
# modifiers=[],
# nesting=False, dont_contract_attribute_symbol='-').next()
graph = nx.relabel_nodes(graph, dict(
zip(graph.nodes(), range(maxnodeid + 1, 1 + maxnodeid + graph.number_of_nodes()))), copy=False)
graph.graph['original']= graphcopy
graph.graph['layer']=self.layer
return graph
